Because K-VQG and Professional K-VQG share the same annotation format, we merged them to train our QG model.
To use region-based image features obtained from the Faster R-CNN [1]}, [2]} in the QG module, we excluded samples with target regions that were not detected as objects by the Faster R-CNN (i.e., the IoU between the detected bounding box and the target bounding box was less than 0.5).
We split the merged K-VQG into train, query, and test sets.
The train set was used to train the OC and QG models and the Oracle Answerer.
The query set was used by the OC model to acquire new knowledge, that is, to evaluate the OC model, generate questions to acquire new knowledge using the QG model, and provide answers using the Oracle Answerer.
The test set was used to evaluate the final performance of the OC model using knowledge acquired from the query set.
The train / query / test splits contained 13,334 / 3,693 / 1,414 images and 16,110 / 4,561 / 1,541 questions, respectively.
The number of known knowledge (i.e., knowledge contained in the train set) was 460, and the number of novel knowledge (i.e., knowledge not contained in the train set) was 49.
We show some detailed statistics of the Professional K-VQG dataset in Table REF .
